Browsing by Subject "Sensor phenomena and characterization"

Now showing 1 - 4 of 4

Open Access
Evaluation of solid-state gyroscope for robotics applications
(Institute of Electrical and Electronics Engineers, 1995-02) Barshan, B.; Durrant-Whyte, H. F.
he evaluation of a low-cost solid-state gyroscope for robotics applications is described. An error model for the sensor is generated and included in a Kalman filter for estimating the orientation of a moving robot vehicle. Orientation eshation with the error model is compared to the performance when the error model is excluded from the system. The results demonstrate that without error compensation, the error in localization is between 5-15"/min but can be improved at least by a factor of 5 if an adequate error model is supplied. Like all inertial systems, the platform requires additional information from some absolute position-sensing mechanism to overcome long-term drift. However, the results show that with careful and detailed modeling of error sources, inertial sensors can provide valuable orientation information for mobile robot applications.
Open Access
Gate bias characterization of CNT-TFT DNA sensors
(IEEE, 2009-12) Aktaş, Özgür; Töral, Taylan
This paper follows the approach in the works of Gui et al. (2007), that use the change in the current of carbon nanotube thin film transistors (CNT-TFT) with DNA attachment and DNA hybridization. The authors have studied the response of CNT-TFTs to DNA binding and hybridization. It was demonstrated for the first time that an increase in sensitivity is observed around the threshold voltage when sweeping the gate bias from negative to positive values. The results presented in this work suggest an improved approach to measuring the response of CNT-TFTs to DNA hybridization.
Open Access
Inertial navigation systems for mobile robots
(Institute of Electrical and Electronics Engineers, 1995-06) Barshan, B.; Durrant-Whyte, H. F.
A low-cost solid-state inertial navigation system (INS) for mobile robotics applications is described. Error models for the inertial sensors are generated and included in an Extended Kalman Filter (EKF) for estimating the position and orientation of a moving robot vehicle. Two Merent solid-state gyroscopes have been evaluated for estimating the orientation of the robot. Performance of the gyroscopes with error models is compared to the performance when the error models are excluded from the system. The results demonstrate that without error compensation, the error in orientation is between 5-15"/min but can be improved at least by a factor of 5 if an adequate error model is supplied. Siar error models have been developed for each axis of a solid-state triaxial accelerometer and for a conducting-bubble tilt sensor which may also be used as a low-cost accelerometer. Linear position estimation with information from accelerometers and tilt sensors is more susceptible to errors due to the double integration process involved in estimating position. With the system described here, the position drift rate is 1-8 cds, depending on the frequency of acceleration changes. An integrated inertial platform consisting of three gyroscopes, a triaxial accelerometer and two tilt sensors is described. Results from tests of this platform on a large outdoor mobile robot system are described and compared to the results obtained from the robot's own radar-based guidance system. Like all inertial systems, the platform requires additional information from some absolute position-sensing mechanism to overcome long-term drift. However, the results show that with careful and detailed modeling of error sources, low-cost inertial sensing systems can provide valuable orientation and position information particularly for outdoor mobile robot applications.
Open Access
Orientation estimate for mobile robots using gyroscopic information
(IEEE, 1994) Barshan, Billur; Durrant-Whyte, H. F.
An error model for a solid-state gyroscope developed in previous work is included in a Kalman filter for improving the orientation estimate of a mobile robot. Orientation measurement with the error model is compared to the performance when no error model is incorporated in the system. The results demonstrate that without error compensation, the error in localization is between 5-15°/min but can be improved by a factor of 5 to 7 if an adequate error model is supplied. Results from tests of this gyroscope on a large outdoor mobile robot system are described and compared to the results obtained from the robot's own radar-based guidance system. Like all inertial systems, the platform requires additional information from some absolute position sensing mechanism to overcome long-term drift. However, the results show that with careful and detailed modelling of error sources, low cost inertial devices can provide valuable orientation and position information particularly for outdoor mobile robot applications.